Each day the amount of data generated by modern societies increases massively. The potential for its correct processing and conversion into relevant information is enormous. With the goal of guiding this process and making it simpler, several data mining proceeding have been proposed. These procedures are of general purpose; therefore, they are designed to be used in a wide range of problems and none of them contains techniques and/or algorithms that fit specific situations. So, important processes such as the categorization of unsupervised instances, in datasets of type attribute-value, are still complex.
In the present research, the phases of a data mining procedure known as CRISP-DM were particularized for the categorization of unsupervised instances from datasets of type attribute-value. CRISP-DM was picked over other existing procedures, such as the KDD process and SEMMA, for being of free distribution, independent of the application and the most used by experts in the field.
Finally, the particularized phases of CRISP-DM were validated with a study case concerning type-2 diabetes mellitus in the province of Cienfuegos. After an initial study, the patients were analyzed, independently, by gender. Results showed three clusters for male patients and four for female patients; all clusters were interpreted as risk levels for future disease complications.